Grad-CAM [1]} uses the gradients of the output with respect to the feature maps of the final convolutional layer to compute the importance of each feature map for the target class \(c\) . The importances of all feature maps are then combined to produce a coarse attribution map of the same size as the final convolutional layer. This attribution map is then upsampled using linear interpolation to match the size of the original image.
Deconvolution [2]} is a modified backpropagation algorithm which only propagates non-negative gradients through ReLU activation functions. Apart from how it handles ReLU activation functions, this method is equivalent to the Gradient method.
Guided Backpropagation (or GuidedBackprop, [3]}) is an extension of the Deconvolution method, where gradients are only backpropagated if they are positive, and if their respective inputs are positive. As such, the normal backpropagation is “guided” by both input signals and output signals.
Guided Grad-CAM [1]} is simply the element-wise product of Guided Backpropagation and upsampled Grad-CAM attributions. This combines the fine-grained attributions of Guided Backpropagations with the localization properties of the attribution maps produced by Grad-CAM.
